AI Code Generation Software Resources
Discussions and Reports to expand your knowledge on AI Code Generation Software
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AI Code Generation Software Discussions
I was curious about how easy or difficult it is to train an AI tool? Feed the tool with all the possible prompts and make it deliver the best possible result in a few seconds. Making an algorithm that cracks everything. Building an expert through experts. Fascinating, please share your thoughts!
The biggest limitation to date is the setup is for 1account. But most of us use the software for more than one Persona or client work. Therefore training it takes time on each one. There are tools that allow you to set up personas and ask from that profile, but the results can be affected depending on the AI tool you are using.
Hi Sheeba,
This is a brilliant question and unfortunately, I can't provide an equally excellent and complete request, as we are just starting on this journey.
In 6 months, I will have a clearer idea of where our organisation is going and what we've achieved since February 2025.
Training an AI tool to deliver optimal results "quickly" by feeding it various prompts is a complex but fascinating process.
Utilizing advanced algorithms and input from diverse experts helps in creating a sophisticated system with the capability to excel in various tasks. This method involves meticulous planning, structured data processing, and continuous refinement to develop an AI that can efficiently tackle a wide array of challenges.
What aspect would you like to analyze or discuss?
A few hours ago, OpenAI released GPT-4.5, currently only for developers and users on the PRO plan.
"GPT-4.5 does not include reasoning, as it was designed to be a more general-purpose, innately smarter model."
https://help.openai.com/en/articles/10658365-gpt-4-5-in-chatgpt
Has anyone started testing it yet? What do you think?
(One of my considerations about the topic, is the cost)
MODEL | Input Cost (per 1M tokens) | Output Cost (per 1M tokens) | Context Window
GPT-4.5 = $75.00 | $150.00 | 128k tokens
GPT-4.o = $2.50 | $10.00 | 128k tokens
Claude 3.7 Sonnet = $3.00 | $15.00 | 200k tokens
I’ve been helping a few dev teams in highly regulated industries evaluate AI code generation tools. In finance and healthcare, speed isn’t the only priority; security, compliance, and auditability are just as important. I went through G2 data and reviews, and here’s what stood out:
- ChatGPT (with Enterprise controls): Teams are using ChatGPT Enterprise or API deployments inside their own secure environments to generate code, documentation, and test cases. It’s attractive because data isn’t used to train the model, and it can be configured to produce code aligned with internal security policies.
- GitHub Copilot: Offers policy controls, privacy protections, and audit logs for teams handling sensitive code. It integrates directly into IDEs and can be limited to known libraries or patterns to reduce security risks.
- Gemini: Being tested by some regulated teams because of its integration with Google Cloud security layers. early adopters like its potential for compliant API generation and code reviews.
- Replit: Good for smaller regulated teams building prototypes in a controlled environment. It helps with boilerplate and test generation but still requires strict oversight before deployment.
- Salesforce Platform (Einstein Copilot for dev): In Salesforce’s ecosystem, Einstein Copilot can generate Apex and Lightning code while adhering to Salesforce’s security standards, which is helpful for healthcare or finance orgs using Salesforce heavily.
Other names worth exploring include Codeium and Amazon CodeWhisperer, which offer enterprise versions with privacy controls and content filtering to help teams generate safer code
For those working in regulated industries: which AI coding tool has been most effective for producing secure, compliant code? Do you rely on enterprise-tier features, private deployments, or manual reviews on top of the generated code?
